{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv(r\"C:\\Users\\yun\\Desktop\\pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.0 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "### Number of occurrences\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc07e770>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\",hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
